The present invention relates to the field of cellular network systems. In particular, the present invention provides for efficient cellular handoffs in cellular networks.
In cellular networks each mobile unit maintains connectivity via an active set of base stations (BS). A handoff mechanism may determine the dynamics of the active set as the mobile unit moves through the network. In a hard handoff, the mobile unit is “handed off” from one BS to another BS as it leaves the cell coverage area of the first BS and enters that of the second BS. In this case, the active set of a mobile unit consists of at most one BS at any given time. Hard handoff mechanisms are used in the GSM and GPRS wireless networking standards and are still under active investigation for use in High Data Rate (HDR) services. Wireless technologies based on CDMA generally employ soft handoff, whereby the mobile unit maintains an active set that may contain multiple BSs. A soft handoff occurs whenever a BS enters or leaves the active set of a mobile unit. Soft handoff mechanisms are used in the IS-95, cdma2000, and WCDMA standards.
To manage the expected increase in subscriber density in future telephone systems, microcells may be used. The introduction of microcells causes some difficulties. The radio propagation characteristics for microcells differ from the macrocellular characteristics because of houses, buildings, and other obstacles disturbing the radio path. For line-of-sight (LOS) handoffs, the mobile always maintains a LOS with both the serving and alternate base stations as shown in FIG. 1. On the other hand, NLOS handoffs arise when the mobile suddenly loses the LOS with both the serving and alternate base station and gains an LOS component from a third base station. As shown in FIG. 2, a NLOS handoff can be due to a so-called corner effect, i.e., there is a 20–30 dB drop of signal level within a 10 m distance while the mobile turns corners in an urban microcellular environment.
Temporal-based handoff mechanisms may yield poor handoff performance in microcells due to the diverse propagation environment and the wide range of user velocities. Consider the NLOS handoff scenario shown in FIG. 2, where a mobile traveling from BS0 has a Rician faded, log-normal shadowed LOS signal from BS0 and a Rayleigh faded, log-normal shadowed, NLOS signal from BS1 until it rounds the corner where the situation is suddenly reversed. The loss of the LOS component may cause a rapid decrease in the signal strength. Handoff mechanisms for this scenario should use shorter temporal averaging windows and larger hysteresis levels so that rapid changes in the mean signal strength can be detected and unnecessary handoffs avoided. Unfortunately, temporal averaging with a short fixed window length gives optimal handoff performance for only a single velocity.
Early work on handoff analysis has largely been based on computer simulation studies. Indeed, in industrial practice, computer simulation remains the primary means for choosing key parameters to optimize the performance of modern-day wireless networks. Detailed computer simulations of wireless cellular networks require considerable computation time, making them cumbersome to use for the purposes of network design and dimensioning.
Vijayan and Holtzman were among the first to propose an analytical model for handoff based on relative signal strength measurements with hysteresis. (See A Model for Analyzing Handoff Algorithms, IEEE Trans. on Vehicular Technology, 42(3):351–356, August 1993). Their model was based on an asymptotic approximation that is inaccurate for smaller hysteresis levels. Subsequently, Zhang and Holtzman proposed an alternative approximate method to analyze handoff based on the Gaussian properties of the received signals. (See Analysis of Handoff algorithms using both absolute and relative measurements. IEEE Trans. on Vehicular Technology, 45(1):174–179, February 1996).
A numerical procedure for analyzing signal strength-based handoff algorithms that computed handoff performance measures of interest was disclosed by Leu and Mark. (See Discrete-time Analysis of Soft Handoff in CDMA Cellular Networks, In Proc. Int. Conf. Comm. '2002, pages 3222–3226, New York City, April/May 2002). This procedure was very efficient for small hysteresis levels, but the computational complexity grew polynomially in the averaging parameter and the hysteresis value.
None of the works mentioned above deal with the corner effects that arise in microcellular urban environments. This effect has been verified by measurements and is characterized by a 20–30 dB drop of signal level in 10–20 meters and appears when a mobile unit turns around a corner and loses the LOS-path to the BS. This important phenomenon must be considered when handoff mechanisms and strategies are discussed.
What is needed is a cellular network handoff decision mechanism that reacts quickly to corner effects in microcellular urban environments and is efficient over a wide range of hysteresis values, avoiding a need for computer simulation of the system.